class_docstr: Mask Head network of Mask R-CNN.
  
  Args:
      n_class (int): The number of classes including background.
      scales (tuple of floats): The scales of feature maps.
class_docstr_tok: ["Mask","Head","network","of","Mask","R-CNN",".","Args",":","n_class","(","int",")",":","The","number","of","classes","including","background",".","scales","(","tuple","of","floats",")",":","The","scales","of","feature","maps","."]
code: def __init__(self, n_class, scales):
      super(MaskHead, self).__init__()
  
      initialW = HeNormal(1, fan_option="fan_out")
      with self.init_scope():
          self.conv1 = Conv2DActiv(256, 3, pad=1, initialW=initialW)
          self.conv2 = Conv2DActiv(256, 3, pad=1, initialW=initialW)
          self.conv3 = Conv2DActiv(256, 3, pad=1, initialW=initialW)
          self.conv4 = Conv2DActiv(256, 3, pad=1, initialW=initialW)
          self.conv5 = L.Deconvolution2D(
              256, 2, pad=0, stride=2, initialW=initialW
          )
          self.seg = L.Convolution2D(
              n_class, 1, pad=0, initialW=initialW
          )
  
      self._n_class = n_class
      self._scales = scales
  
file_path: chainercv/chainercv/links/model/fpn/mask_head.py
imports: from __future__ import division
  import numpy
  import chainer
  from chainer.backends import cuda
  import chainer.functions
  from chainer.initializers import HeNormal
  import chainer.links
  from chainercv.links import Conv2DActiv
  from chainercv.utils.bbox.bbox_iou import bbox_iou
  from chainercv.links.model.fpn.mask_utils import mask_to_segm
  from chainercv.links.model.fpn.mask_utils import segm_to_mask
label_desc: Use image node_id 1 to create a new MaskHead object from inherited base classes: chainer with example: obj = MaskHead(n_class, scales)